Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models

--Existing driving style recognition systems largely depend on low-level sensor-derived features for training, neglecting the rich semantic reasoning capability inherent to human experts. T o bridge this gap, we propose a novel framework that integrates Semantic Privileged Information (SPI) derived from large language models (LLMs) to align recognition outcomes with human-interpretable reasoning. First, we introduce DriBehavGPT, an interactive LLM-based module that generates natural-language descriptions of driving behaviors. These descriptions are then encoded into machine learning-compatible representations via text embedding and dimensionality reduction. Finally, we incorporate them as privileged information into Support V ector Machine Plus (SVM+) for training, enabling the model to approximate human-like interpretation patterns. Importantly, SPI is exclusively used during training, while inference relies solely on sensor data, ensuring computational efficiency without sacrificing performance. COGNIZING driving styles plays a pivotal role in understanding human-vehicle interactions, thereby improving personalized driving experience and enhancing the acceptance of advanced driver assistance systems [1]. For example, adaptive cruise control systems offer configurable parameters, such as inter-vehicle distance, target speed, and driving modes, to accommodate both aggressive drivers prioritizing traffic throughput efficiency and conservative drivers emphasizing safety [2], [3].